2004 IEEE International Symposium on Cluster Computing and the Grid
DWDM-RAM: Enabling Grid Services with Dynamic Optical Networks
S. Figueira', S. Naiksata", H. Cohen', D. Cutrell', P. Daspit', D. Gutierrez3, D. B. Hoang4,
T. Lavian', J. Mambretd, S. Merrill', F. Travostino'.
'Santa Clara University, 'Nortel Networks Labs,''Stanford University,
4
University of Technology, Sydney, 'iCAIR and Northwestern University.
{sfgueira, snaiksatam] @scu.edu, {hcohen, dcutrell, pduspit, tlavian, smerrill,
tra vos) @ Tiortelnetworks.com, degm @ stanford. edu, dhoang @ it.uts. edu.au,
j-mambretti@northwestt?m.edu.
among multiple sites. One of the harriers to achieve
this goal is the fact that traditional technology used in
currently deployed networks limits at many levels the
transfer of massive amounts of data. Although
availability of sufficient bandwidth is an issue, access
to resources that provides this bandwidth solves only
one part of the problem. The complete solution is a
new network architecture with the ability to
orchestrate data flows with many different types of
characteristics,
including
those
requiring
exceptionally high bandwidth for sustained periods
of time. In the DWDM-RAM model, these high
performance data flows are provided by dedicated
optical paths, which are dynamically allocated. State
of the art DWDM technology based on dynamic
wavelength switching enables the creation of Grid
services that allocate and release these paths either
on-demand or by advance reservation.
Grid applications typically need to allocate and
reserve multiple types of resources, such as
computational. data, instrumentation, and networks,
at distributed sites. Services such as the Globus
Resource Allocation Manager (GRAM) job scheduler
[8] have been developed to coordinate and schedule
the computational and data resources needed by Grid
applications. In [ U ] ,the authors propose G A M , the
General Purpose Architecture for Reservation and
Allocation, which enables the construction of
application-level co-reservation and co-allocation
libraries that applications can use to dynamically
schedule collections of resources. In G A M , the
network is treated as a primary resource, which can
be allocated and reserved as well, and a service
provides this resource.
T o enable the efficient use of optical networks as
a primary resource, a novel architecture for the
reservation and dynamic allocation of individual
Abstract
Advances in Grid technology enable the deployment
of data-intensive distributed applications, which
require moving Terabytes or even Petabytes of data
between data banks. The current underlying
nehvorks cannut provide dedicated links with
adequate end-to-end sustained bandwidth to support
ihe requirements of these Grid applications. DWDMRAM' is a novel service-oriented architecture, which
harnesses the enormous bandwidth potential of
optical nefivorks and demonstrates their on-demand
nsage on the OMNlner. Preliminary experiments
suggest that dynamic optical networks, such as the
OMNlnet, are the ideal option for transferring such
massive amounts of data. DWDM-RAM incorporates
an OGSI/OGSA compliant service interface and will
promote greater convergence between dynamic
optical networks and data intensive Grid computing.
1. Introduction
DWDM-RAM is a DARPA [IO] funded research
experiment established to design and implement in
prototype a new type of Grid service architecture that
is optimized to support data intensive Grid
applications through advanced optical networking.
DWDM-RAM closely integrates Grid data services
and optical networking services.
One of the goals of Grid architecture development
is to enable efficient support for data-intensive
applications, which may require moving - without
prior notice - Terabytes or even Petabytes of data
' DWDM-RAM research was made possible with suppon from
DARPA. award NO. ~3116nz-98.z-11194.
0-7803-8430-W04/$20.0002004 IEEE
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2004 IEEE International Symposium on Cluster Computing and the Grid
2. Optical Networks
wavelengths is required. This architecture relies on a
new network service, which provides options for
allocating dedicated network resources. This service
must discover the nctwork topology and both explore
the availability and optimize the schedule of the
optical network resources. It should also present a
standardized, high-level and network-accessible
interface. A natursl choice for implementing this
interface is the Open Grid Service Interface (OGSI)
[27]. Such interfaces are compliant with the GGFs
OGSA specification [I31 and, in addition, conform to
widely used Web Services standards (WSDL, SOAP,
and XML).
In addition to presenting an OGSI-compliant
interface, the network service should have a standard
way of representing wavelength resources for
communicating with clients. No such standard
currently exists, For the Grid community, a
promising approach would he to extend the XML
form of the Resource Specification Language (RSL)
[16]. This RSL schema is currently used by GRAM
for other resources. Adding optical network
extensions to RSL would make it possible to enhance
GRAM to handle optical network resources.
This paper presents an implementation of the
aforementioned concepts as a part of the DWDMRAM research project. This project encompasses the
development of two services, the Data Transfer
Scheduling (DTS) service and the Network Resource
Scheduling (NRS) service. The DTS service's goal is
to provide scheduled data transfer between data
banks. The NRS service's goal is to provide dynamic
dedicated wavelength, both on demand and by
advance reservation. The DTS uses the NRS service
to provide the user application with an optimized
utilization of the optical network. A preliminary
implementation of this architecture was first
demonstrated during the 9' Global Grid Forum, in
Chicago, October 2003 and then at the
Supercomputing Conference, in Phoenix, November
2003. Other results of experiments with dynamic
on advanced optical networks
wavelength
provisioning for data-intensive Grid applications
have been recently published [22].
This paper is organized as follows: Section 2
describes dynamic optical networks and their
features, Section 3 introduces the DWDM-RAM
architecture, Section 4 presents the interface created
to allocate wavelengths dynamically, Section 5
describes the testhed used in the implementation and
presents initial results, and Section 6 concludes and
discusses future work.
Bulk data transfer oriented grid computing requires
quality of service, in particular, guaranteed minimum
bandwidth and minimized packet loss, which are not
easily achievable in packet switching networks.
Recently, Grid developers have been using IntServ
[6] and DiffServ [4] approaches to obtain QoS in
packet switching networks ([I 11[121[151[211[251).
IntServ and DiffServ may help in some special cases,
but they arc far from perfect solutions. Optical
networks have the potential to change the way
networks are used in Grid computing. Optical
networks based on wavelength switching can easily
provide guaranteed bandwidth and performance in
terms of low hit error rates. In fact, based on the
same assumption, other groups have also been
focusing on providing optical networks to the Grid
community [26][29].
A main concern about wavelength switched
optical networks is the overhead incurred during the
end-to-end path setup. However, it should he noted
that the time taken in the transfer of the huge
amounts of data generated by Grid applicarions
amortizes the time spent i n the setup. The graph in
Figure 1 illustrates this fact. It shows the amount of
time spent in the path setup as a percentage of the
total transfer time versus data transfers of different
sizes. These numbers were generated synthetically in
order to motivate the usage of optical networks in
different scenarios. We indicate in the graph the
threshold at which the setup time is about 5% of the
total time, i.e., the overhead is amortized and
becomes insignificant.
100%
80%
t 40%
500GB
30%
2 20%
2
10%
0%
1w
,ow
10000
100000
File Size (ME)
lOww0
lWoOW0
Figure 1: Overhead is insignificant at 500 GB
In Figure I , we assume a setup time of 48 sec and
a bandwidth of 920 Mhps, in which case the
threshold is 500 GB. Since Grid applications usually
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2004 IEEE International Symposium on Cluster Computing and the Grid
transfer huge data sets, it is clear that the time for the
path setup is negligible in Grid computing.
Note that 48 sec is the currently observed average
upper-bound time for the path setup, and 920 Mbps is
the currently observed maximum throughput (see
Section 5). Of equal importance is the time required
to deallocate resources to enable subsequent usage,
once the data transfer is complete. This has been
observed to be around l l s e c (see Section 5) and is
also insignificant in comparison to the data transfer
time.
3. The DWDM-RAM Architecture
The DWDM-RAM architecture shown in Figure 2
closely fits the Grid Architecture described in [14].
This architecture also leverages the Globus Toolkit
3’s functionality. The main modules are explained
below.
Fabric: OMNInet and ODIN
The OMNInet photonic testhed network [3]
implemented across a metro area constitutes the
shared resource in the DWDM-RAM architecture.
Optical Dynamic Intelligent Network* (ODIN)
1.17][18][22] is the software suite that controls the
OMNInet through various lower-level API calls
presented in Section 5 . ODIN has been designed for
extremely high performance, long term Terabit data
Ilows with flexible and fine grained control. It is a
stateless server implementation, which includes a
simple API to provide path provisioning and
monitoring to the higher layers.
Figure 2: The DWDM-RAM architecture
Resource: DWDM-RAM Network Resource
Scheduling (NRS) Service
The Resource and Connectivity protocol layers form
the neck of our “hourglass model” [23] based
architecture. The NRS is essentially a resource
management service, which supports protocols that
offer advance reservations and QoS. It maintains
schedules and provisions resources in accordance
with the schedule. It provides an OGSMOGSI
compliant interface to request the optical network
resources. It has complete understanding of dynamic
lightpath provisioning and communicates these
requests to the Fabric layer entities. In our
architecture, the NRS can stand alone without the
DTS (see Collective layer). GARA integration is an
important avenue for future development. A key
feature is the ability to reschedule reservations,
which satisfy under-constrained scheduling requests,
in order to accommodate new requests and changes
in resource availability. Requests may be underconstrained through specification of a target
reservation window, which is larger than the
requested
duration,
through
open-ended
specifications, such as “ASAP”, or through more
sophisticated request specifications, such as clientprovided utility functions for evaluating scheduling
Connectivity: TCP/IP, HTTP, SOAP, JAXRPC
Currently, the DWDM-RAM system uses standard
off-the-shelf communication protocol suites to
communicate requests and responses between
application clients and DWDM-RAM services and
also among DWDM-RAM components. Requests
and responses conform to OGSI and Web Services
specifications, i.e., they are SOAP messages, carried
in HTTP envelopes and transported over TCPflP
connections. The SOAP messages are encoded using
IAX-RPC to facilitate interoperability between
existing Java-based Globus toolkit and the DWDMRAM components.
’ODIN research was made passible with suppan from the National
Science Foundation: awwd ANI-0123399
-
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2004 IEEE International Symposium on Cluster Computing and the Grid
costs. Models for priorities and accounting can also
be incorporated into the scheduling algorithms.
that provides allocation of bandwidth in IntServ
networks. Our approach follows their model but
develops a more comprehensive schedule and
reservation based Network Resource Scheduling
(NRS) service, which provides user applications with
access to underlying optical network resources. This
service should guarantee dedicated access to the
optical links, which may be requested on-demand or
by advance reservation. Advance reservation requests
can he under-constrained, which means the request
can be satisfied by more than one possible time slot.
This allows the service to reschedule reservations as
needed to satisfy future requests and changing
conditions.
The NRS also attempts to provide an interface
with different levels of detail. Some coordinating and
scheduling clients may need only high-level facilities
like those for individual applications. For these
applications, the interface should allow the request of
light paths, hut these clients do not need to have
knowledge of the details of the underlying network
topology or management protocols. However, clients
that attempt to optimize network resources use may
need a richer interface, e.g., they may need to be able
to schedule individual optical segments. For these
clients, the interface should allow the request of
individual segments. Although these clients may
need knowledge of the network topology, they
should still b e insulated from the network
management protocols.
We have started by implementing a network
service with a simple application-level interface. This
service is able to allocate dynamically dedicated endto-end light paths requested on demand. The
following were the goals for this initial interface:
It should be usable by Grid applications.
It should be network-accessible from any
authorized host.
It should be standards-based and not require any
proprietary technology.
It should allow the application to specify
parameters of the desired optical path that are
relevant at the application level.
It should hide the details of the underlying
network topology and network management
protocols.
It should support the immediate allocation of
bandwidth, but should be easy to extend in order
to incorporate future scheduling.
At the highest level, the interface described here is
wrapped in a Java implementation that shields the
caller from the details of the application-level
protocols, which are needed to communicate with the
Collective: DWDM-RAM Data Transfer
Scheduling (DTS) Service
The DTS service is a direct extension of the NRS
service. The DTS shares the same hackend
scheduling engine and resides on the same host. It
provides a higher-level functionality that allows
applications to schedule advance reservations for
data transfers without the need to directly schedule
path reservations with the NRS service. The DTS
service requires additional infrastructure in the form
of services living at the data transfer endpoints.
These services perform the actual data transfer
operations once the network resources are allocated.
As with the NRS, the DTS has an OGSI interface,
and GARA integration is a planned avenue for future
development. As an extension of the NRS, the DTS
also employs under-constrained scheduling requests
in order to accommodate new requests and changes
in resource availability.
It is important to note that, in the DWDM-RAM
design, the application at the endpoints drives the
statc and decisions by DTS, NRS, and ODIN, thus in
full compliance with the end-to-end argument [24].
These intervening layers never attempt to reconstruct nor second-guess an application's intent
from traffic. Instead, they follow the explicit
command and control from the application. These
layers are best positioned to appreciate both
application's commands and network status, while
bridging meaningfully between the two.
Also, we plan to have DWDM-RAM comply with
the so-called fate-sharing principle [7].This design
principle argues that it is acceptable to lose the state
information associated with an entity if, at the same
time, the entity itself is lost. In this case, DTS, NRS,
and ODD4 will make a point of reclaiming resources
once an application is terminated, or is found to he
non-responsive for a configurable time interval.
4. Dynamically
Allocating
Bandwidth
On-Demand
For wavelength switching to be useful for Grid
applications, a network service with an application
level interface is required to request, release and
manage the underlying network resources. Previous
work has been done in defining an OGSNOGSI
based network service interface to network resources.
In 1121, for example, the authors describe a service
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2004 IEEE International Symposium on Cluster Computing and the Grid
well insulated from either OGSI, or WS-RF, or any
other emerging specification that may prevail. In any
case, the user of the NRS service is spared any direct
contact with the underlying middleware.
service. The service itself hides various lower level
network topology and management protocols from
the caller.
This current interface exposes two methods to the
user:
allocateNetworkPath
and
deallocateNetworkPath.
The
method
allocateNetworkPath requests a path between
the end hosts. The path allocated should meet the
criteria specified in the parameter object passed to the
method. These parameters include the network
addresses of the hosts to he connected, the minimum
and maximum acceptable bandwidths, and the
maximum duration of the allocation. The
allocateNetworkPath method returns to the
caller an object containing descriptive information on
the path that was allocated. This object also serves as
a handle for the deallocateNetworkPath
rcquest. By adding parameters representing earliest
and latest acccptable start times, this interface can he
extended to accommodate under-constrained advance
reservations.
The NRS interface suggests likely properties for a
service that may implement this interface. Such a
service can he implemented in a way that does not
maintain any session or context information for any
particular client between calls. The only necessary
context information is the allocated path identifier,
which the client is required to supply in order to
deallocate a path. The service must maintain this
information ahout these allocated paths so, in this
sense, it is not “stateless,” but each client call can he
treated as a self-contained unit and processed entirely
in a single-message exchange. Thus, the interface
tits the service oriented architecture of Web Services
[5] quite closely.
At the same time, we believe that it is important to
conForm to the emerging Open Grid Services
Architecture (OGSA) Grid standard to be accessible
to Grid applications that use this architecture. Hence
the NRS interface also conforms to the Open Grid
Services Infrastructure (OGSI) interface for services.
This interface, in its basic form, is a Web Service
interface with some additional conventions from the
OGSI standard. Although client access is via a
normal Java interface as described above. internally,
the client-service interface is an OGSI Web Service
implemented using SOAP and the JAX-RPC API.
The OGSI implementation is quite lean (no SDEs),
and therefore the conversion to new specifications
like the WS-Resource Framework (WS-RF) [301 will
be a somewhat mechanical process. In fact, the
DWDM-RAM architecture builds the concept of a
Network Service from the ground up and is fairly
5. Testbed, Experiments and Result
The testbed for the DWDM-RAM system is provided
by a next-generation large-scale MetroLAN dynamic
optical network extent called the OMNInet [3],
operational since 2001. OMNInet uses metro dark
fiber infrastructure, which has been provided by SBC
to connect locations sponsored by Northwestern
University, and a mix of product and rcsearch
equipmcnt from Nortel Networks. OMNInet has been
deployed at four locations in the Chicago metro area
and is configured in a partial-mesh IOGE DWDM
network. Each location has a MEMS based 8 x 8
DWDM photonic switch interfaced with a Nortel
Passport 8600 Ethernet Routing Switch, providing 4
x lOGE line interfaces and up to 32 x IGE client
interfaces. The OMNInet configuration is shown in
Figure 3.
Univ oflllinois, Chiraxo
Northwestern Unir
uu
Figure 3 OMNlnet core nodes
The dynamic light path provisioning of the
OMNlnet is provided by the Optical Dynamic
Intelligent Network (ODIN) services module, which
manages the optical network control plane and
resource provisioning including path allocation,
deletion, and setting of attributes. ODIN uses an O F
UNI 1.0 [28] compliant UN1 library to interface with
the OMNInet unified control plane. The control plane
is orchestrated by a GMPLS [20] based CR-LDP [ I ]
implementation, which is used to establish the label
forwarding state along the routes computed by an
enhanced
OSPF
(constraint-based
routing)
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2004 IEEE International Symposium on Cluster Computing and the Grid
algorithm5. (Note: An RSVP-TE [2] implementation
to support advance reservations is also in the process
of being deployed.) The technological breakthrough
in the design of the OMNInet photonic switches,
coupled with the tight integration of the control
software, brings down the provisioning time from
months to a few seconds. This is the critical enabling
factor in on-demand provisioning, which makes
dynamic optical networks an important technology
for data-intensive Grid computing.
Figure 4 compares the efficiency of packet
switching with lambda switching, executing with
different amounts of bandwidth, by plotting the
amount of data transferred against the time taken in
seconds. This graph was generated synthetically. The
path setup takes 48 sec which was recorded in
experiments (explained in detail later). The path
setup time includes the time required to request a
path, allocate the path, configure the layer I and 2
components of the network and update the relevant
tables. It is important to note that only a miniscule
fraction (approximately 20 milliseconds) of this delay
is actually due to the setting up of the path within the
optical switch. A part of the setup delay is also due to
the propagation of state information through the
photonic nodes and optical paths. The major portion
of the delay is introduced by switches at the edge of
the optical network, which switch the data to and
from the end hosts. This is an artifact of the current
network setup, and with fairly simple optimizations,
we expect to bring this 48 sec delay down
significantly.
1
5000
45W
-PlhkBI
mdtcned
1% M W
--Lamu
4oW
g- 35W
e
The important information obtained by this graph
is the crossover points at which the optical network
becomes a better option than the packet switched
network. Note that when the path setup takes 48 sec,
the crossover point can he with files as small as 1.9
GB and up to 4.5 GB. The graph affirms the
enormous advantage of the DWDM-RAM system
over other packet switching systems when
transferring large data sets and presents an exciting
option for the Grid community.
Note that, for transferring smaller amounts of
data, the packet switching traditional approach may
be a better option. The appropriate network to use, in
each case, should bc decided by co-allocation
services, as defined in [9][ 121. These co-allocation
scheduling services use information about the
resource's capabilities and the application's
requirements to allocate a performance-efticient set
of resources for the application.
Figure S presents a throughput graph for a 30 GB
memory-to-memory transfer. The smooth curve is the
average effective data-transfer rate, including the
startup cost of allocating the lightpath. The jagged
curve, averaging around 440 Mbps, is the
instantaneous throughput, measured in 0.1 sec
intervals on the receiving machine. Note that the
throughput periodically jumped to about 530 Mhps
for a sustained period of about 100 seconds, a
phenomenon under investigation. For this 30 GB data
transfer, we can see that the startup costs begin to be
amortized after about 230 sec or 12.2 GB.
amshed
ISW M W
3oW
$ =W
Imched
I750 Mbw)
moo
r w h e d (l
GbW
15W
1000
. . .
. ' .
..
. ... .
I1oGbpSl
5w
0
0
20
40
60
rim(S)
80
1W
Figure 5: Instantaneous and average throughput for a
30 GB memoly-to-memory transfer over OMNlnet
using TCP as transport protocol.
120
Figure 4: Packed Switched.vs. Lambda Network -Setup time tradeoffs.
The above experiment was run on dual PIII
997MHz machines with S12MB memory, 1GB swap
space, running on Red Hat Linux 7.3 (Kernel: 2.4.183")
and using I GigE NIC cards. Note that we
For the OMNlnet metro photonic networking research, the
GMPLS. 0-UNI. LMP smdards we implemented as Nortel
Networks proprietary Wavelength Routing and Distribution
Protocol (WRDP)optical control plane software suite.
used the Linux networking protocol stack with no
performance tuning.
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PO04 IEEE International Symposium on Cluster Computing and the Grid
When performing file transfer in the above setup
we observed large oscillations in the instantaneous
throughput, which resulted in significantly lower
average throughput. We suspected that this was an
artifact of using standard TCP. This prompted us to
experiment using UDT [ 191, a high performance data
transfer protocol. Figure 6 shows a 15 GB memoryto-memory data transfer using UDT as the underlying
transport protocol. The throughput has been sampled
at 0.1 sec intervals. The maximum and the average
throughput recorded were 920 Mhps and-680 Mbps,
respectively.
of the channel. Currently, the default UDT protocol
values are being used, namely, UDT buffer size of
40,960,000 bytes, UDP send buffer of 64KB. UDP
receive buffer of 4 MB, and flow window size of
25600. We believe that tuning these parameters to the
OMNInet testhed will yield a higher throughput.
I
Table 1: Auplication
level measurements recorded for
..
a 20GB file transfer
Event
I Seconds
Start : File transfer request arrives
0.0
1
Path Allocation request
ODIN server processing
Network reconfiguration
Data transfer
Path deallocation request
I
0.5
2.6
45
622
0.3
r&ource accessed through a Grid service, which is
necessary for data-intensive applications. The
DWDM-RAM system presented here addresses
limitations of traditional implementations and
facilitates on demand optical channel (lightpath)
- .
provisioning. We have also presented an
OGSAJOGSI compliant interface developed to ease
the usage of the OMNInet by Grid applications.
The interface presented meets the needs of many
applications, hut it should be extended to include
features that may be necessary or desirable in other
circumstances. One of our main goals is to make the
interface G A M compliant and able to provide a
comprehensive, re-schedulable and window-based,
network service as discussed in Section 3.
We are currently exploring UDT and other high
performance transport protocols which can help to
achieve a uniform throughput close to the maximum
limits. This, coupled with the facility to aggregate
several channels to transfer one large tile, will
dramatically impact the data transfer time perceived
by the user.
While a photonic switched network like OMNInet
may be ideal for a DWDM-RAM-like architecture,
our experiments suggest that DWDM-RAM could
also he implemented on a traditional opticalelectrical-optical (OEO) network. Providing a solid
interface to such optical networks is a key step in
enabling its usage by the Grid community. Our initial
T-1-1
Figure 6: Instantaneous throughput recorded for a 15
GB memory-to-memory transfer over OMNlnet using
UDT a s transport protocol.
The numbers in Figure 6 show a better
performance than those in Figure 5 , reinforcing our
initial suspicion that TCP was affecting the
throughput.
We also performed file transfer
experiments using UDT. For a 20 GB tile transfer,
the maximum and the average throughput recorded
were 376 Mbps and 272 Mbps respectively. These
are significantly lower than those seen in Figure 6,
leading us to believe that issues like disk YO on both
ends of the data pipe, buffering, and similar factors
impact the average throughput in the disk-to-disk file
transfers.
Table 1 shows a breakup of the total end-to-end
time required for the above file transfer operation to
complete. It is important to note that the lightpath
used to transfer data in the experiment described was
dynamically allocated and dedicated to the file
transfer.
While UDT does seem to have had a favorable
impact on the average throughput, it is evident from
the graph of Figure 6, that there are still oscillations
in the instantaneous throughput. Also, the average
throughput is less than the maximum 1 Gbps capacity
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2004 IEEE International Symposium on Cluster Computing and the Grid
Integration,” Open Grid Service Infrastructure WG,
Global Grid Forum, June 2002.
[I41 1. Foster et al., ‘The Anatomy of the Grid Enabling
Scalable Virtual Organizations.” lnremationul Joumal
of Superrompurer Applications, 15(3), 2001.
[IS] 1. Foster et al., “A Quality of Service Architecture that
Combines Resource Reservation and Application
Adaptation,” 8th International Workshop on Qualiy
of Service, 2000.
[I61 “The Globus Resource Specification Language RSL
VI.O,” http://www.globus.org/gram/rsI_specl .html.
[I71 R. Grossman et al., “Experimental Studies Using
Photonic Data Services at Grid 2002.” Joumal of
Future Computer Systems. Elsevier Press, pp.945-956,
August 2003.
[IS] R. Grossman et al., “Photonic Data Services:
Integrating Path, Network and Data Services to
Support Next Generation Data Mining Applications,”
NGDMM, Proceedings, November 2002.
[I91 Y. Gu and R. Grossman, “End-to-End Congestion
Control for High Performance Data Transfer.”
submitted to IEEWACM Transactions on Networking.
[ZO] L. Berger (Ed), “Generalized Multi-Protocol Label
Switching
(GMPLS)
Signaling
Functional
Description.” RFC 3471, January 2003.
[211 G. Hoo et al., “QoS as Middleware: Bandwidth
Reservation System Design,’’ Proceedings of rhe 8th
IEEE Symposium on High Perfurmunce Distributed
Computing, pp. 345-346, 1999.
[22] I. Mambretti et al., “The Photonic TeraStream:
Enabling Next Generation Applications Through
Intelligent Optical Networking at Grid 2002,” Joumul
of Future Computer Systems, Elsevier Press. August
2003, pp.897-908.
[23] “Realizing the Information Future: The Internet and
Beyond,” National Academy Press, 1994,
experiments have been extremely promising, and we
are continuing to improve the network service
Acknowledgements
The authors would like to thank Aaron Johnson, Fei
Yeh, Jeremy Weinberger, Jim Chen, and David
Lilithun from ICAIR, and Ramesh Durairaj, Inder
Monga, and Phil Wang from Nortel Networks, for
their invaluable contributions.
References
[I] P.
Ashwood-Smith (Ed) and L. Berger (Ed),
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